Arterial Road Incident Detection Based on Time-Moving Average Method in Bluetooth-Based Wireless Vehicle Reidentification System

Abstract

Incident detection algorithms, which are an essential part of traffic management systems, have been studied for several decades, but the research focus has primarily been on algorithms for incident detection on freeways and other free-flowing roads. When applied on arterial roads, the achievement of stable performance and scalability are major challenges when developing an effective incident detection algorithm. In this research, the authors propose an incident detection algorithm that utilizes travel time and traffic volume samples generated from a Bluetooth-based wireless vehicle reidentification system that has been implemented on arterial roads. The proposed algorithm is based on a moving average over time, which can recognize sample travel time and traffic volume patterns resulting from incidents. The use of a moving average overcomes limitations resulting from sparse travel time sample data collected. Within the algorithm, a threshold strategy is applied that makes the algorithm easy to implement and transfer, which is an important requirement for practitioners. The proposed algorithm is evaluated using reported accident data and the insight of two traffic engineers, and provides a good balance between detection rate and false-alarm rate.